Predictive intake modelling
Predictive intake modelling uses mathematical modelling strategies to estimate intake of food, personal care products, and their formulations.
Definition
Predictive intake modelling seeks to estimate intake of products and/or their constituents which may enter the body through various routes such as ingestion, inhalation and absorption.
Predictive intake modelling can be applied to determine trends in food consumption and product use for the purpose of extrapolation.
Applications
A predictive intake modelling approach is used to estimate voluntary food intake (VFI) by animals where their eating habits cannot be exactly measured.[1][2] For humans, predictive intake modelling is used to make estimations of intake from foods,[3] pesticides,[4] cosmetics[5] and inhalants[6] as well as substances that can be contained in these like nutrients, functional ingredients, chemicals and contaminants.
Predictive intake modelling has applications in public health, risk assessment and exposure assessment, where estimating intake or exposure to different substances can influence the decision making process.
Predictive intake modelling strategies
Regression approach
The regression analysis approach is based on estimations through extrapolation or interpolation where there is a cause-and-effect relationship found by data fitting. These trends tend to be phenomenological.
Mechanistic modelling approach
A mechanistic modelling approach is one where a model is derived from basic theory. Examples of these include compartmental models which can be used to describe the circulation and concentration of airborne particles in a room or household for estimating intake of inhalants.[7]
Population-based approach
A population-based approach tracks consumer intake from individual members of a sample population over time. Mathematical models are used to combine these habits and practices databases with separate databases on product or food formulation to probabilistically estimate intake or exposure for the sample population. Moreover, survey weights may be applied to each subject in the study based on their age, demographic and location allowing the sample of subjects to correctly represent an entire population, and thus estimate intake for that population.
References
- ↑ T. J. Hackmann and J. N. Spain, “A mechanistic model for predicting intake of forage diets by ruminants.,” Journal of animal science, vol. 88, no. 3, pp. 1108–24, Mar. 2010.
- ↑ S. Yoosuk, H. B. Ong, S. W. Roan, C. a. Morgan, and C. T. Whittemore, “A simulation model for predicting the voluntary feed intake of a growing pig,” Acta Agriculturae Scandinavica, Section A - Animal Science, vol. 61, no. 4, pp. 168–186, Dec. 2011
- ↑ H. G. Schutz, “Prediction of nutritional status from food consumption and consumer attitude data.,” The American journal of clinical nutrition, vol. 35, no. 5 Suppl, pp. 1310–8, May 1982
- ↑ P. Shade and P. Georgopoulos, “Using inhalation dosimetry models to predict deposition of ultrafine particles,” Ozobe Research Centre Science Workshop January 26, 2007, 2007. [Online]. Available: http://ccl.rutgers.edu/ccl-files/presentations/2007-01-26_ORC-Workshop-at-DEP/ShadePamela_ORC-NJDEP_poster_2007.01.26.pdf. [Accessed: 27-Nov-2013]
- ↑ S. Grégoire, C. Ribaud, F. Benech, J. R. Meunier, a Garrigues-Mazert, and R. H. Guy, “Prediction of chemical absorption into and through the skin from cosmetic and dermatological formulations.,” The British journal of dermatology, vol. 160, no. 1, pp. 80–91, Jan. 2009
- ↑ J. J. Van Hemmen, “Predictive exposure modelling for pesticide registration purposes,” Annels of Occupational Hygeine, vol. 37, no. 5, pp. 541–564, 1993
- ↑ M. Singal, “RIFM 2-Box Indoor Air Dispersion Model Is An Alternative Method To Calculate Inhalation Exposure To Fragrance,” Research Institute for Fragrance Materials, 2012. [Online]. Available: http://www.rifm.org/press-detail.php?id=68. [Accessed: 28-Nov-2013]